{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T07:32:40Z","timestamp":1774423960518,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":46,"publisher":"ACM","license":[{"start":{"date-parts":[[2017,8,4]],"date-time":"2017-08-04T00:00:00Z","timestamp":1501804800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"MIT-Lincoln Labs"},{"name":"Miller Institute for Basic Research in Science University of California Berkeley"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2017,8,4]]},"DOI":"10.1145\/3097983.3098047","type":"proceedings-article","created":{"date-parts":[[2017,8,4]],"date-time":"2017-08-04T18:35:54Z","timestamp":1501871754000},"page":"35-44","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":58,"title":["Learning Certifiably Optimal Rule Lists"],"prefix":"10.1145","author":[{"given":"Elaine","family":"Angelino","sequence":"first","affiliation":[{"name":"University of California, Berkeley, Berkeley, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicholas","family":"Larus-Stone","sequence":"additional","affiliation":[{"name":"Harvard University, Cambridge, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Alabi","sequence":"additional","affiliation":[{"name":"Harvard University, Cambridge, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Margo","family":"Seltzer","sequence":"additional","affiliation":[{"name":"Harvard University, Cambridge, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cynthia","family":"Rudin","sequence":"additional","affiliation":[{"name":"Duke University, Durham, NC, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2017,8,4]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Learning certifiably optimal rule lists for categorical data. Preprint at arXiv:1704.01701 (April","author":"Angelino E.","year":"2017","unstructured":"E. Angelino , N. Larus-Stone , D. Alabi , M. Seltzer , and C. Rudin 2017. Learning certifiably optimal rule lists for categorical data. Preprint at arXiv:1704.01701 (April 2017 ). E. Angelino, N. Larus-Stone, D. Alabi, M. Seltzer, and C. Rudin 2017. Learning certifiably optimal rule lists for categorical data. Preprint at arXiv:1704.01701 (April 2017)."},{"key":"e_1_3_2_2_2_1","volume-title":"echnical Report. bibinfoinstitutionR.P.I. Math Report No. 214","author":"Bennett K. P.","unstructured":"K. P. Bennett and J. A. Blue 1996. Optimal Decision Trees. bibinfotype T echnical Report. bibinfoinstitutionR.P.I. Math Report No. 214 , Rensselaer Polytechnic Institute . K. P. Bennett and J. A. Blue 1996. Optimal Decision Trees. bibinfotypeTechnical Report. bibinfoinstitutionR.P.I. Math Report No. 214, Rensselaer Polytechnic Institute."},{"key":"e_1_3_2_2_3_1","volume-title":"Machine learning: Between accuracy and interpretability. Learning, Networks and Statistics","author":"Bratko I.","unstructured":"I. Bratko . 1997. Machine learning: Between accuracy and interpretability. Learning, Networks and Statistics . International Centre for Mechanical Sciences, Vol . Vol. 382 . Springer Vienna , 163--177. I. Bratko. 1997. Machine learning: Between accuracy and interpretability. Learning, Networks and Statistics. International Centre for Mechanical Sciences, Vol. Vol. 382. Springer Vienna, 163--177."},{"key":"e_1_3_2_2_4_1","unstructured":"L. Breiman J. H. Friedman R. A. Olshen and C. J. Stone 1984. Classification and Regression Trees. Wadsworth.  L. Breiman J. H. Friedman R. A. Olshen and C. J. Stone 1984. Classification and Regression Trees. Wadsworth."},{"key":"e_1_3_2_2_5_1","unstructured":"C. Chen and C. Rudin. 2017. Optimized Falling Rule Lists and Softly Falling Rule Lists. (2017). shownoteWork in progress.  C. Chen and C. Rudin. 2017. Optimized Falling Rule Lists and Softly Falling Rule Lists. (2017). shownoteWork in progress."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1998.10473750"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1013916107446"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1214\/09-AOAS285"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1022641700528"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/B978-1-55860-377-6.50023-2"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1037\/0003-066X.34.7.571"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/85.2.363"},{"key":"e_1_3_2_2_13_1","unstructured":"D. Dobkin T. Fulton D. Gunopulos S. Kasif and S. Salzberg 1996. Induction of shallow decision trees. (1996).  D. Dobkin T. Fulton D. Gunopulos S. Kasif and S. Salzberg 1996. Induction of shallow decision trees. (1996)."},{"key":"e_1_3_2_2_14_1","unstructured":"A. Farhangfar R. Greiner and M. Zinkevich. 2008. A Fast Way to Produce Optimal Fixed-Depth Decision Trees International Symposium on Artificial Intelligence and Mathematics (ISAIM 2008).  A. Farhangfar R. Greiner and M. Zinkevich. 2008. A Fast Way to Produce Optimal Fixed-Depth Decision Trees International Symposium on Artificial Intelligence and Mathematics (ISAIM 2008)."},{"key":"e_1_3_2_2_15_1","volume-title":"Generating Accurate Rule Sets Without Global Optimization Proceedings of the Fifteenth International Conference on Machine Learning (ICML '98)","author":"Frank Eibe","unstructured":"Eibe Frank and Ian H . Witten 1998 . Generating Accurate Rule Sets Without Global Optimization Proceedings of the Fifteenth International Conference on Machine Learning (ICML '98) . 144--151. Eibe Frank and Ian H. Witten 1998. Generating Accurate Rule Sets Without Global Optimization Proceedings of the Fifteenth International Conference on Machine Learning (ICML '98). 144--151."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2594473.2594475"},{"key":"e_1_3_2_2_17_1","volume-title":"Efficient Algorithms for Constructing Decision Trees with Constraints Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'98)","author":"Garofalakis M.","unstructured":"M. Garofalakis , D. Hyun , R. Rastogi , and K. Shim . 2000 . Efficient Algorithms for Constructing Decision Trees with Constraints Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'98) . 335--339. M. Garofalakis, D. Hyun, R. Rastogi, and K. Shim. 2000. Efficient Algorithms for Constructing Decision Trees with Constraints Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'98). 335--339."},{"key":"e_1_3_2_2_18_1","volume-title":"Proceedings of the ECML-98 Workshop on Upgrading Learning to Meta-Level: Model Selection and Data Transformation. 78--85","author":"Giraud-Carrier C.","year":"1998","unstructured":"C. Giraud-Carrier . 1998 . Beyond predictive accuracy: What? . In Proceedings of the ECML-98 Workshop on Upgrading Learning to Meta-Level: Model Selection and Data Transformation. 78--85 . C. Giraud-Carrier. 1998. Beyond predictive accuracy: What?. In Proceedings of the ECML-98 Workshop on Upgrading Learning to Meta-Level: Model Selection and Data Transformation. 78--85."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"crossref","unstructured":"B. Goodman and S. Flaxman 2016. European Union regulations on algorithmic decision-making and a \"right to explanation\" ICML Workshop on Human Interpretability in Machine Learning (WHI).  B. Goodman and S. Flaxman 2016. European Union regulations on algorithmic decision-making and a \"right to explanation\" ICML Workshop on Human Interpretability in Machine Learning (WHI).","DOI":"10.1609\/aimag.v38i3.2741"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1022631118932"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2010.12.003"},{"key":"e_1_3_2_2_22_1","volume-title":"Cost-sensitive and Interpretable Dynamic Treatment Regimes Based on Rule Lists Proceedings of the Artificial Intelligence and Statistics (AISTATS).","author":"Lakkaraju H.","year":"2017","unstructured":"H. Lakkaraju and C. Rudin 2017 . Cost-sensitive and Interpretable Dynamic Treatment Regimes Based on Rule Lists Proceedings of the Artificial Intelligence and Statistics (AISTATS). H. Lakkaraju and C. Rudin 2017. Cost-sensitive and Interpretable Dynamic Treatment Regimes Based on Rule Lists Proceedings of the Artificial Intelligence and Statistics (AISTATS)."},{"key":"e_1_3_2_2_23_1","unstructured":"J. Larson S. Mattu L. Kirchner and J. Angwin. 2016. How We Analyzed the COMPAS Recidivism Algorithm. ProPublica (2016).  J. Larson S. Mattu L. Kirchner and J. Angwin. 2016. How We Analyzed the COMPAS Recidivism Algorithm. ProPublica (2016)."},{"key":"e_1_3_2_2_24_1","volume-title":"textitLearning Certifiably Optimal Rule Lists: A Case For Discrete Optimization in the 21st Century. (2017). shownoteUndergraduate thesis","author":"Larus-Stone N. L.","unstructured":"N. L. Larus-Stone . 2017. textitLearning Certifiably Optimal Rule Lists: A Case For Discrete Optimization in the 21st Century. (2017). shownoteUndergraduate thesis , Harvard College . N. L. Larus-Stone. 2017. textitLearning Certifiably Optimal Rule Lists: A Case For Discrete Optimization in the 21st Century. (2017). shownoteUndergraduate thesis, Harvard College."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1214\/15-AOAS848"},{"key":"e_1_3_2_2_26_1","volume-title":"IEEE International Conference on Data Mining","author":"Li W.","year":"2001","unstructured":"W. Li , J. Han , and J. Pei 2001 . CMAR: Accurate and efficient classification based on multiple class-association rules . IEEE International Conference on Data Mining (2001), 369--376. W. Li, J. Han, and J. Pei 2001. CMAR: Accurate and efficient classification based on multiple class-association rules. IEEE International Conference on Data Mining (2001), 369--376."},{"key":"e_1_3_2_2_27_1","volume-title":"Integrating classification and association rule mining Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining (KDD '98). 80--96","author":"Liu B.","unstructured":"B. Liu , W. Hsu , and Y. Ma 1998. Integrating classification and association rule mining Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining (KDD '98). 80--96 . B. Liu, W. Hsu, and Y. Ma 1998. Integrating classification and association rule mining Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining (KDD '98). 80--96."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.5555\/1046920.1088687"},{"key":"e_1_3_2_2_29_1","unstructured":"R.S. Michalski. 1969. On the quasi-minimal solution of the general covering problem Proceedings of the Fifth International Symposium on Information Processing. 125--128.  R.S. Michalski. 1969. On the quasi-minimal solution of the general covering problem Proceedings of the Fifth International Symposium on Information Processing. 125--128."},{"key":"e_1_3_2_2_30_1","unstructured":"New York Civil Liberties Union. 2014. Stop-and-Frisk Data. (2014). shownotehttp:\/\/www.nyclu.org\/content\/stop-and-frisk-data.  New York Civil Liberties Union. 2014. Stop-and-Frisk Data. (2014). shownotehttp:\/\/www.nyclu.org\/content\/stop-and-frisk-data."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-010-0174-x"},{"key":"e_1_3_2_2_32_1","unstructured":"J. R. Quinlan. 1993. C4.5: Programs for Machine Learning. Morgan Kaufmann.  J. R. Quinlan. 1993. C4.5: Programs for Machine Learning. Morgan Kaufmann."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-010-5168-9"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00058680"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2007.06.004"},{"key":"e_1_3_2_2_36_1","unstructured":"C. Rudin and S. Ertekin 2015. Learning Optimized Lists of Rules with Mathematical Programming. (2015). shownoteUnpublished.  C. Rudin and S. Ertekin 2015. Learning Optimized Lists of Rules with Mathematical Programming. (2015). shownoteUnpublished."},{"key":"e_1_3_2_2_37_1","first-page":"3384","article-title":". Learning Theory Analysis for Association Rules and Sequential Event Prediction","volume":"14","author":"Rudin C.","year":"2013","unstructured":"C. Rudin , B. Letham , and D. Madigan 2013 . Learning Theory Analysis for Association Rules and Sequential Event Prediction . Journal of Machine Learning Research Vol. 14 (2013), 3384 -- 3436 . C. Rudin, B. Letham, and D. Madigan 2013. Learning Theory Analysis for Association Rules and Sequential Event Prediction. Journal of Machine Learning Research Vol. 14 (2013), 3384--3436.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1214\/10-STS330"},{"key":"e_1_3_2_2_40_1","volume-title":"The Decision List Machine. In Advances in Neural Information Processing Systems (NIPS '03)","volume":"15","author":"Sokolova M.","unstructured":"M. Sokolova , M. Marchand , N. Japkowicz , and J. Shawe-Taylor . 2003 . The Decision List Machine. In Advances in Neural Information Processing Systems (NIPS '03) , Vol. Vol. 15 . 921--928. M. Sokolova, M. Marchand, N. Japkowicz, and J. Shawe-Taylor. 2003. The Decision List Machine. In Advances in Neural Information Processing Systems (NIPS '03), Vol. Vol. 15. 921--928."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"crossref","unstructured":"K. Vanhoof and B. Depaire 2010. Structure of association rule classifiers: A review Proceedings of the International Conference on Intelligent Systems and Knowledge Engineering (ISKE '10). 9--12.  K. Vanhoof and B. Depaire 2010. Structure of association rule classifiers: A review Proceedings of the International Conference on Intelligent Systems and Knowledge Engineering (ISKE '10). 9--12.","DOI":"10.1109\/ISKE.2010.5680784"},{"key":"e_1_3_2_2_42_1","volume-title":"Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.","author":"Vellido A.","year":"2012","unstructured":"A. Vellido , J. D. Mart\u00edn-Guerrero , and P. J.G. Lisboa . 2012 . Making machine learning models interpretable . In Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. A. Vellido, J. D. Mart\u00edn-Guerrero, and P. J.G. Lisboa. 2012. Making machine learning models interpretable. In Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning."},{"key":"e_1_3_2_2_43_1","volume-title":"2015natexlaba. Causal Falling Rule Lists. Preprint at arXiv:1510.05189 (Oct","author":"Wang F.","year":"2015","unstructured":"F. Wang and C. Rudin . 2015natexlaba. Causal Falling Rule Lists. Preprint at arXiv:1510.05189 (Oct . 2015 ). F. Wang and C. Rudin. 2015natexlaba. Causal Falling Rule Lists. Preprint at arXiv:1510.05189 (Oct. 2015)."},{"key":"e_1_3_2_2_44_1","volume-title":"Falling Rule Lists Proceedings of Artificial Intelligence and Statistics (AISTATS).","author":"Wang F.","unstructured":"F. Wang and C. Rudin . 2015natexlabb . Falling Rule Lists Proceedings of Artificial Intelligence and Statistics (AISTATS). F. Wang and C. Rudin. 2015natexlabb. Falling Rule Lists Proceedings of Artificial Intelligence and Statistics (AISTATS)."},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.5555\/3305890.3306086"},{"key":"e_1_3_2_2_46_1","volume-title":"CPAR: Classification based on predictive association rules Proceedings of the 2003 SIAM International Conference on Data Mining (ICDM '03). 331--335.","author":"Yin X.","year":"2003","unstructured":"X. Yin and J. Han . 2003 . CPAR: Classification based on predictive association rules Proceedings of the 2003 SIAM International Conference on Data Mining (ICDM '03). 331--335. X. Yin and J. Han. 2003. CPAR: Classification based on predictive association rules Proceedings of the 2003 SIAM International Conference on Data Mining (ICDM '03). 331--335."},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1111\/biom.12354"}],"event":{"name":"KDD '17: The 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","location":"Halifax NS Canada","acronym":"KDD '17","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3097983.3098047","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3097983.3098047","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T03:30:25Z","timestamp":1750217425000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3097983.3098047"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,8,4]]},"references-count":46,"alternative-id":["10.1145\/3097983.3098047","10.1145\/3097983"],"URL":"https:\/\/doi.org\/10.1145\/3097983.3098047","relation":{},"subject":[],"published":{"date-parts":[[2017,8,4]]},"assertion":[{"value":"2017-08-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}